For several decades, there has been a general trend in IT towards functional convergence. This trend also had a major impact on analytics, especially in recent years. For many years, the data warehouse (DWH) was considered the central data platform. However, new trends such as self-service BI and, later, machine learning and AI quickly showed that the DWH was no longer able to cope with these trends.
Quickly, the statement that is often (mistakenly) attributed to Charles Darwin was proven correct here as well: It is not the smartest or strongest that survive, but those that adapt best to change. The DWH was not able to adapt quickly enough, and separate and fragmented self-service solutions quickly emerged alongside the DWH. Additionally, AI and ML solutions never really made their way into the DWH. The DWH was thus no longer a strategic data platform for companies, but part of a larger ecosystem.
That was the situation a few years ago. Now, most technology providers have evolved and introduced new technological platforms and concepts. In the wake of the “Big Data wave”, the concept of data lakes was born. Initially, people focused almost exclusively on storing large amounts of data, while analyzing the data was unfortunately only an afterthought. As a result, the first big data lakes very quickly turned into data swamps, which were of limited use for data analysis.
This is where the advanced analytics and machine learning vendors came to the rescue. They shifted the focus from purely storing large amounts of data to building data platforms that allow actual analysis and integration of machine learning. The goal was converged platforms that allow all kinds of varying analytical workloads. Furthermore, cloud platform providers rethought existing paradigms using concepts that originated primarily from agile software development and combined them with the techniques of classic DWH and data lakes.
The result fits the general IT trend towards functional convergence and is increasingly being referred to as “Unified Data & Analytics Platform” (UDAP), Data Lakehouse or Data Fabric.
The Beginning of a Revolution
Of course, these new data platforms still offer all the logical functions of classic DWHs, but now the DWHs are only a smaller part of an overall architecture. Furthermore, this new platform is the foundation for all AI-based digital transformation offensives. State-of-the-art AI solutions often require massive and flexible computation power, which classic platforms could not provide.
Modern data platforms fix this problem and manage to link data of varying quantity, structure and type in one place. This data can originate from a wide variety of source systems or directly from Internet of Things (IoT) devices and is the ideal basis for data science projects. New methods such as DevOps, MLOps or Continuous Integration and Continuous Delivery (CI/CD) are also part of these data platforms and provide the agility and implementation speed that companies urgently need today.
With our many years of experience in designing and implementing modern data platforms, we will find the best solution for you, implement it, and integrate it into your processes and IT landscape. We offer future-proof solutions that meet the latest AI standards, optimally fulfill your requirements, and will open up new potentials for you.
Whether large or small, our certified architects have successfully designed and implemented numerous different data platforms.
Take advantage of our proven architectural design process: Don’t reinvent the wheel, build on our proven design blueprints.
Our platform experts not only design but can also fully implement the platform for you – with a constant focus on your benefits.